library(tidyverse) # ecosystem of data science packages
library(ggiraph) # to make ggplots interactiveGlobal Warming Accelerating
1 Description
The data visualization of this module is based on the scatter plot that appears in the following New York Times article by Raymond Zhong and Nadia Popovich (Dec. 26, 2023)
Earth Was Due for Another Year of Record Warmth. But This Warm?
https://www.nytimes.com/2023/12/26/climate/global-warming-accelerating.html
1.1 Details (datasheet)
- Topic(s):
- Weather
- Climate Change
- Global Warming
- Temperature
- Data:
- Size: medium data
- Format: raw data in CSV format
- Graphic:
- Type: scatter plot
- Styles: ggplot, interactive ggiraph
- Interactive: no
1.2 R Packages
2 Data
The source of the data is NOAA Global Time Series
https://www.ncei.noaa.gov/access/monitoring/climate-at-a-glance/global/time-series
In particular, time series 1850 - 2024 are available in 3 formats:
We decided to keep it simple and work with the CSV file. A local copy is in the file global-temp-time-series.csv stored in the data/ folder:
# import CSV file
dat = read_csv(
file = "data/global-temp-time-series.csv",
skip = 4,
show_col_types = FALSE)For convenience purposes, we need to add columns year and month
# add year and month columns
dat = dat |>
mutate(Year = as.numeric(str_sub(Date, start = 1, end = 4)),
Month = as.numeric(str_sub(Date, start = 5, end = 6)))
head(dat)# A tibble: 6 × 4
Date Anomaly Year Month
<dbl> <dbl> <dbl> <dbl>
1 185001 -0.45 1850 1
2 185002 -0.21 1850 2
3 185003 -0.21 1850 3
4 185004 -0.35 1850 4
5 185005 -0.29 1850 5
6 185006 -0.08 1850 63 Graphics
We go over a series of graphics using "ggplot2", starting with a simple version and then gradually adapting the code to get closer to the graphics in the NYTimes article.
3.1 Graphic 1
The first graphic is just a simple scatter plot just to make sure that we get a visualization that makes sense in terms of general appearance.
# sanity check
ggplot(dat, aes(x = Year, y = Anomaly)) +
geom_hline(yintercept = 0, color = "gray30") +
geom_point()
3.2 Graphic 2
The first tuning operations involve modifying the shape of the points so that we can change their color (border) and fill attributes. To be more precise, we map Anomaly (temperature) to fill. Likewise, we simplify the theme:
ggplot(dat, aes(x = Year, y = Anomaly)) +
geom_hline(yintercept = 0, color = "gray30") +
geom_point(shape = 21, color = "white", aes(fill = Anomaly)) +
theme_minimal()
3.3 Graphic 3
A major component of this scatter plot has to do with the color of the points using a color gradient palette. For testing purposes, we’ve decided to explore the visual appearance with a viridis scale:
# viridis palette just for testing purposes
ggplot(dat, aes(x = Year, y = Anomaly)) +
geom_hline(yintercept = 0, color = "gray30") +
geom_point(shape = 21, color = "white", aes(fill = Anomaly)) +
scale_fill_viridis_c() +
theme_minimal()
3.4 Graphic 4
The target graphic has a gradient scheme that ranges from green-blue to orange-brown. One way to customize the fill color is with the scale function scale_fill_gradient2()
ggplot(dat, aes(x = Year, y = Anomaly)) +
theme(panel.background = element_blank(),
legend.position = "none",
panel.grid.minor = element_blank(),
panel.grid.major = element_line(linetype = "dotted", color = "gray50")) +
geom_hline(yintercept = 0, color = "black", linewidth = 0.3) +
geom_point(shape = 21, color = "white", aes(fill = Anomaly)) +
scale_fill_gradient2(low = "#41948d", mid = "#f6f7e6", high = "#c75626",
breaks = seq(from = -1, to = 1.5, by = 0.2)) +
labs(x = "", y = "") +
scale_x_continuous(breaks = c(seq(from = 1850, to = 2000, by = 25), 2024))
3.5 Graphic 5
To get a finer gradient scheme, we use scale_fill_gradientn() and provide a vector of 10 color hues as follows:
ggplot(dat, aes(x = Year, y = Anomaly)) +
theme(panel.background = element_blank(),
legend.position = "none",
panel.grid.minor = element_blank(),
panel.grid.major = element_line(linetype = "dotted", color = "gray50")) +
geom_hline(yintercept = 0, color = "black", linewidth = 0.3) +
geom_point(shape = 21, color = "white", aes(fill = Anomaly)) +
scale_fill_gradientn(
colors = c("#5bada5", "#86b9b0", "#d9e7dc", "#f4f1e6", "#f7e4d0",
"#f7d2b2", "#f0a66f", "#e88245", "#c75626", "#a62623")) +
labs(x = "", y = "") +
scale_x_continuous(breaks = c(seq(from = 1850, to = 2000, by = 25), 2024))
3.6 Graphic 6
Another less-ideal alternative with by using the function scale_fill_steps2(). In addition, we include the text annotations, and the rectangular segment to highlight a set of 12 points corresponding to the 12 months of year 1973:
# custom color palette with scale_fill_steps2()
# this is the "good one"
ggplot(dat, aes(x = Year, y = Anomaly)) +
theme(panel.background = element_blank(),
legend.position = "none",
panel.grid.minor = element_blank(),
panel.grid.major = element_line(linetype = "dotted", color = "gray50")) +
geom_hline(yintercept = 0, color = "black", linewidth = 0.3) +
geom_point(shape = 21, color = "white", aes(fill = Anomaly)) +
scale_fill_steps2(low = "turquoise3", mid ="#faf8e6", high = "#f0692b",
midpoint = 0.05, breaks = seq(from = -1, to = 1.5, by = 0.2)) +
labs(x = "", y = "") +
scale_x_continuous(breaks = c(seq(from = 1850, to = 2000, by = 25), 2024),
position = "top") +
geom_rect(xmin = 1972.5, xmax = 1973.5, ymin = 0, ymax = 0.35,
color = "gray30", fill = "#FFFFFF00", linewidth = 0.1) +
annotate(geom = "text", x = 1973, y = 0.39, label = "12 months", size = 2) +
annotate(geom = "text", x = 1878, y = 0.8, size = 3, hjust = 0,
label = "Monthly global temperature compared\nwith average for the 20th century")